Fiscal equalisation schemes and sub-central government borrowing
Bibliographic record
Abstract
This paper analyses the role played by the fiscal equalisation scheme in determining sub-national\npublic borrowing in decentralised countries. We show theoretically how the regional income redistribution\nmodifies the intertemporal budget constraint of the regions and discuss the conditions under which the federal\nequalisation arrangements are likely to lead to diverging borrowing between rich and poor regions. We test\nempirically the link between regional government primary balances and the level of GDP per capita in Canada,\nGermany and Spain. Our econometric analysis shows that this relationship can be either positive (as in the\nGerman case) or negative (as in the Canadian and Spanish cases), thus suggesting that either poor or rich regions\ncan display higher regional public borrowing on average. We attribute these results to the differences in the\ndesign of the fiscal equalisation schemes and illustrate this through numerical simulations of our model. These\nresults suggest that reforms of the federal financing schemes can prove instrumental in reducing regional\nheterogeneity in public borrowing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".